Method and system for obtaining geographic characterization data

ABSTRACT

Payment transaction data for consumers who make purchases in a geographical region is used, together with information about restaurants the consumers have made payments to, to obtain statistical data characterizing the restaurant habits of the consumers. This information may be useful for designing marketing operations, or to individuals contemplating opening a restaurant in the region.

FIELD OF THE INVENTION

The present invention relates to computer systems andcomputer-implemented methods for obtaining data characterizingpurchasing habits of consumers in a geographical location.

BACKGROUND OF THE INVENTION

Several automated methods exist for obtaining information about thepurchasing habits of individual consumers, based on their spendingbehavior. This can be used for targeting product advertising to theindividual consumers based on their previous purchases. Additionally, itcan be used to identify market trends, such as that there is increasingmarket demand for a certain product. The term “product” is used in thisdocument to include both objects (i.e. physical products), data productsand services.

SUMMARY OF THE INVENTION

The present invention aims to provide new and useful computer systemsand computer implemented inventions for obtaining information aboutpurchasing behavior.

In general terms, the present invention proposes defining at least onegeographical region, and using payment transaction data for consumerswho make purchases in that region, and information about competingmerchants who offer products in the region, to obtain statistical datacharacterizing payment habits of the consumers in the region(“geographical region characterization data”).

Thus, in contrast to conventional automated mechanisms for gatheringinformation about the purchasing behavior of consumers, which as notedabove tends to obtain information about individual consumers, or about amarket as a whole, the present invention makes it possible to obtaininformation characterizing a geographical region.

This information may be useful in several ways. Firstly, it can be usedby existing merchants in the geographical region to plan commercialactivities to obtain more customers, such as by offering products in anew commercial range, or by sending out targeted advertising in thegeographical region.

It would further permit potential merchants who may be consideringobtaining an outlet in the geographical region to obtain informationabout the consumers of the region, to enable them to make a decisionabout whether to open an outlet in the geographical region, and if so,how to tailor it to the consumption preferences of the consumers. Forexample, a merchant who already operates, or is considering operating,one or more outlets fitting one or more of the categories, may scancorresponding geographical region characterization data for each of aplurality of regions, to find one which matches the properties of theoutlets of the merchant.

In a preferred form of the invention the merchants are businessestablishments where meals or refreshments may be purchased. Suchmerchants are here referred to as “restaurants”. Typically, but notalways, the restaurants provide facilities on which the food may beconsumed (i.e. they are eat-in restaurants, rather than take-awayrestaurants). The meals or refreshments are typically pre-cooked (a termwhich is used to include pre-baked), and are typically served warm,although invention is applicable also to restaurants (such as juice barsand certain vegetarian restaurants) where the meals or refreshments arenot pre-cooked or served warm.

A specific expression of the invention is a computer-implemented methodfor obtaining geographic region characterization data statisticallycharacterizing the consumption habits of consumers in a geographicalregion, the method including:

-   -   (i) identifying a plurality of consumers associated with the        region,    -   (ii) obtaining, for each of a plurality of merchants who perform        sales in the region, the merchants being competitors in a        commercial sector, corresponding merchant characterization data        which indicates whether the commercial activity of the merchant        belongs to one of more of a set of merchant categories, and    -   (iii) using payment transaction data for the identified        consumers, and the merchant characterization data, to obtain        respective geographical region characterization data describing        statistically the purchasing behavior of the identified        consumers in relation to the merchant categories.

As noted above, in a preferred form of the invention the merchants arerestaurants. In this case, the merchant categories include (a)categories associated with respective types of cuisine (e.g. a nationalcuisine such as “Italian”, “Indian”, or “French”; or a cuisine definedbased on ingredients, such as “vegetarian” or “vegan”; note that acertain restaurant may belong to multiple ones of these categories, e.g.if it sells Indian vegetarian food), (b) categories indicative of price(e.g. “high price” “economy”), and/or (c) categories associated withtype of premises (e.g. diner, café, pub, take-away, exclusiverestaurant).

The method may include a step of ranking the merchants based on thepayment transaction data, thereby identifying popular merchants. Thestep of obtaining the geographical region characterization data (i.e.step (iii) in the list of steps given above) may then exclude merchantswho are below a threshold in the popularity ranking.

The payment transaction data ranking may be based on a function of anyone or more of the total money spent at the merchant (e.g. during apredetermined period), the number of payment transactions (e.g. in thepredetermined period), and/or the average amount spent per transaction.

The step of obtaining the geographical region characterization data mayinclude a clustering step, whereby the geographical regioncharacterization data comprises data indicating one of more “clusters”of consumers, that is sets of consumers whose spending behavior meets atleast one similarity criterion. The set of consumers may have acardinality (i.e. the number of consumers in the set) which is above athreshold. The threshold may be chosen to be sufficiently high, and/orthe at least one similarity criterion may be chosen to be sufficientlystrict, that the cluster is of statistical significance. That is, theprobability that it arose purely by chance as an artefact of the paymenttransaction data, without reflecting a true clustering of consumerbehavior, is below a likelihood threshold, e.g. less than 5%.

The step of obtaining the geographical region characterization data mayemploy demographic information describing the identified consumers, sothat the geographical region characterization information is indicativeof the purchasing behavior in relation to the categories of those of theidentified consumers who are in at least one demographic category.

The demographic data may be used in the clustering procedure (if any).That is, at least one of the similarity criteria may be indicative ofdemographic similarity. In this way, clusters of the data may beassociated with respective demographic groups.

The merchant characterization data may include information obtainedautomatically from a consumer data website, such as social mediawebsite.

The payment transaction data refers to a payment made using a paymentcard. As used in this document, the term “payment card” refers to anycashless payment device associated with a payment account, such as acredit card, a debit card, a prepaid card, a charge card, a membershipcard, a promotional card, a frequent flyer card, an identification card,a prepaid card, a gift card, and/or any other device that may holdpayment account information, such as mobile phones, smartphones,personal digital assistants (PDAs), key fobs, transponder devices,NFC-enabled devices, and/or computers.

The invention may be expressed in terms of a method performed by acomputer-system automatically, or as a computer-system arranged toperform the method. The term “automatic” is used here to meansubstantially without human involvement except with regard to initiationof the method, such as by defining the categories which are used in step(iii).

BRIEF DESCRIPTION OF THE FIGURES

An embodiment of the invention will now be described for the sake ofexample only with reference to the following drawings, in which:

FIG. 1 shows a computerized network including a server or performing amethod which is an embodiment of the invention;

FIG. 2 is a flowchart of the method which is an embodiment of theinvention;

FIG. 3 is a representation of data obtained using the method of FIG. 2;and

FIG. 4 shows schematically the technical architecture of a server forperforming the method of FIG. 2.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Referring firstly to FIG. 1, a computerized network is shown including aserver 1 which can perform a method which is an embodiment of theinvention. The method is explained below with reference to FIG. 2. Theserver 1 is associated with a payment network for processing paymenttransactions made using payment cards issued by corresponding issuerbanks. These include payment transactions involving restaurants.

The computerized network is capable of performing a known paymentprocess which is as follows. Typically, a consumer who holds a paymentcard issued by an issuer bank makes a payment by presenting his or herpayment card to a POS terminal 2 operated by a merchant. For simplicityonly one POS terminal is included in FIG. 1, but in actuality thecomputerized network typically contains thousands of POS terminals,operated by corresponding merchants. The POS terminal 2 sends a messageto a server 3 of an acquirer bank, where the merchant maintains anaccount, including the details of the payment card (possibly inencrypted form) and the amount of the payment. The acquirer bank server3 sends a message to the payment network server 1, again including thedetails of the payment card and the amount of the payment. The paymentserver 1 determines the issuer bank which issued the payment card, andsends an authorization request to an issuer bank server 4 operated bythe issuer bank. For simplicity only a single issuing bank server 4 isillustrated in FIG. 1, although in actually there will be many suchservers associated with respective issuing banks. The issuer bank server4 either authorizes the transaction, or declines it, and in any casesends a corresponding message to the payment network server 1, whichpasses the message to the acquirer bank server 3, which forwards it tothe POS terminal 2. If the decision of the issuer bank server 4 was toauthorize the transaction, then the acquirer bank credits the paymentamount (possibly less a handling charge) to the account of the merchant,and the issuer bank 4 debits it from an account associated with thepayment card. At a later time (typically during a clearing operation)the issuer bank 4 makes a payment to the acquirer bank. The paymentnetwork server 1 retains details of the payment transaction in adatabase 5.

Unlike a conventional payment network server, the payment network server1 is able to access a further merchant database 6 containing data foreach of a plurality of merchants, and a consumer database 7 containingdata for each of a plurality of consumers (all of whom carry one or morecorresponding payment cards). In the following description it is assumedthat the merchants each operate one or more restaurants (typically ofthe kind in which food is consumed on the premises, but possibly alsotake-away restaurants), but in variations of the embodiment themerchants could be active in another commercial sector in which they arecompetitors.

The data in the merchant database 6 includes details of the paymentaccounts associated with the corresponding merchants. The database 6further includes a geographical location of each restaurant. Themerchant database 6 further includes merchant characterization dataindicating, for each of the merchants, which of a number of categoriesthe restaurant falls into: (i) a plurality of categories based on thetype of cuisine sold by the restaurant (e.g. there may be respectivecategories for each of a number of national cuisines such as “Chinese”,“Italian”, “Pub food” or “Indian food”; there may also be categoriesbased on ingredients, such as “vegetarian” or “non-vegetarian” or“vegan”; a given restaurant will typically be in multiple categories),(ii) a plurality of categories corresponding to respective price rangesfor the restaurant (e.g. “budget”, or “high price”, and (iii) aplurality of categories corresponding to respective restaurant types(e.g. a diner, café, take-away, pub, family restaurant, or an exclusive(“elegant”) restaurant).

The data in the merchant database 6 may optionally be obtained from anexternal source (e.g. an existing database of restaurants, such as onemaintained by a restaurant chain), or it may be generated by a carryingout a survey for restaurants in certain geographic regions(s). Anotherpossibility however is that the payment network server 1 uses acommunication network 8 such as the internet to obtain the informationfrom at least one consumer website 9, which may be a social mediaconsumer website such as Yelp, Zagat, Zomato etc which aggregatesinformation submitted by multiple consumers.

The consumer database 7 contains a residence address associated witheach of the consumers. The residence address is typically obtained fromthe issuer bank of the payment card. The consumer database 7 furthercontains demographic data characterizing the payment card holders. Thismay include an age range of the payment card holder, his/her gender,his/her marital status (and any more information which may be availableabout his/her family circumstances), and financial information relatingto the payment card holder such as his/her salary or data characterizinghis/her payment behavior using the payment card (e.g. average monthlyspend using the payment card). Payment transactions by payment cardholders for whom demographic information is missing from the consumerdatabase 7 may optionally not be used in the method of FIG. 2.

Turning to FIG. 2, a flow chart is shown which shows the steps of amethod 100 which is an embodiment of the invention. The invention may beperformed by the server 1, or indeed by any other computer system whichhas access to the data in the databases 5, 6, 7. The method is carriedout in respect of at least one specified geographic region (e.g. acertain town, a certain postcode/zipcode or range of postcodes/zipcodes,or regions defined for use in the method). Optionally, the database 6may only contain data in respect of restaurants for whom thegeographical location is in the at least one specified geographicalregion. If data in respect of restaurants is present, that data is notused in the method 100.

Each of the geographic region(s) has a size which is in accordance witha typical distance which consumers are prepared to travel to arestaurant. For example, its extent (i.e. which may be defined as thelength of the longest straight line which can be drawn entirely withinthe geographic region) may be at least 50 m, or at least 100 m, and/orat most 10 km, at most 5 km, or at most 2 km. Note that the sizes of thegeographical region(s) may differ from each other, e.g. so as to belarger in rural areas.

The geographical region(s) may be selected in a pre-step of the method(not shown in FIG. 2). The size(s) of the geographical region(s) mightbe different for certain categories of restaurant. For example, theymight be chosen to be small for a fast-food restaurant and larger for ahigh-end restaurant.

In step 101, the server 1 attempts to assign the consumers for whompayment transaction data exists in the database 5 to one of thespecified geographic regions (e.g.zip code areas). Consumers who are notassigned to any of the specified geographic regions are not consideredfurther in the method 100, and the data about them and theirtransactions is not used in the method.

The assignment of consumers to the specified geographic regions may bedone simply by checking whether the consumers' residential addresses arewithin one of the specified geographical regions.

Alternatively, and more preferably, step 101 uses a code model in whicha consumer is assigned to a geographical region based both on theresidential address and on the locations at which the consumer spendsmoney according to the payment transaction data in the database 5. Thiswould avoid the risk of a consumer being assigned to a geographicallocation where he or she happens to live but where he or she makesalmost no purchases (e.g. because it is rural, and because he or shespends almost all their money in a neighboring town).

For example, a consumer may be allocated to a specified geographicalregion if at least a certain proportion of all the spending which theconsumer performs within a certain radius of the residential address, iswithin the geographical region.

Note that unlike an assignment process which is based entirely upon thelocations of merchants at which the payment card is used (i.e. one whichdoes not use the consumer's residential address at all), an assignmentprocess based both on the residential address and on the paymenttransactions in a locality of the residential address, reduces the riskof a consumer being assigned to a geographical region where he or sherarely goes but which contains an organization at which he or she makesmajor purchases (e.g. remotely). In other words, it increases the chancethat the consumer will be assigned to a particular geographical regionbased on everyday spending, rather than large exceptional spending.

Another way of doing this would be to assign a consumer to ageographical regions by recognizing geographical clustering in thelocations at which the consumer makes payments according to the paymenttransaction data.

A further possibility would be for the server 1 to perform step 101using (at least partly) data associated with the payment card. Forexample, many payment cards are associated with a permanent accountnumber (PAN). In some cases the PAN links the payment card to a bankassociated with a certain geographical region. For example, a paymentcard from a German bank is typically associated with a German cardholder. If such a payment card is used in another geographical region(e.g. a geographical region in the US), the server 1 may identify thecard holder as a temporary visitor in the geographical region.Optionally, the server 1 may exclude transaction data from that paymentcard in the following process.

The possible implementations of step 101 discussed above identifyconsumers who are resident in or near the geographical location as wellas having spent money there. An alternative would be to identifyconsumers associated with the geographical region as consumers who havemade purchases in (or near) a geographical region but who are notresident there. One way of doing this would be for step 101 to excludefrom the identified consumers any consumer whose residence address iswithin the geographical region (and optionally also consumers whoseresidence address is within a certain distance from the geographicalregion). Alternatively, if step 101 is performed using the PAN number ofthe payment card (as mentioned above), the server 1 may identify theconsumers as ones who have made a purchase in the geographical regionbut whose PAN number is associated with an issuing bank which is distantfrom the geographical region (e.g. in another country).

The set of steps 102-104 are performed separately for each of thespecified geographical regions, using only the data in the databases 5,6, 7 relating to the consumers assigned to the specified geographicalregion, and the restaurants located there.

In step 102, for all the consumers assigned to a given specifiedgeographical region, the payment transactions of the consumers to therestaurants in that region are used to derive a popularity index foreach of the restaurants. This may be defined as a function of any one ormore of total spend, total number of transactions or ticket size (i.e.average spend per transaction). The restaurants are then ranked based onthe popularity index.

In optional step 103, it is determined whether for any of therestaurants high in the ranking (e.g. with a popularity index above athreshold), the database 6 is lacking merchant characterization data. Ifso, it is supplemented by drawing information from the consumerwebsite(s) 9 over the communication network 8.

In step 104, using the popularity index and the merchantcharacterization data, as well optionally as demographic data from thedatabase 7, a clustering algorithm is used to obtain statisticalinformation about the restaurant spending habits of consumers in thegeographic region. The statistical information is not consumer-specific;but represents statistically significant numerical values characterizingthe restaurant spending behavior of typical consumers who make purchasesin, and live in (or near to), the geographical location. Alternatively,if, as mentioned above, the consumers identified in step 101 as beingassociated with the geographical region, are ones who have madepurchases there, but who are not resident there (or near there), thenthe statistical information will characterize the restaurant spendingbehavior of visitors to the geographical location.

A typical result is shown in FIG. 3, showing that in respect of acertain geographical region, four statistically significant clusters ofrestaurant spending have been identified: budget Chinese, family Italianrestaurants, exclusive Indian restaurants, and pub spending. This ispresented as a pie diagram, in which each of the clusters is representedby a respective segment of a circle, which subtends a respective angleat the center of the circle which represents its respective statisticalsignificance. Any of these clusters may, for example, be indicative ofthe number of restaurants of the corresponding type in the region beinghigh, and/or the total number of payment transactions at restaurants ofthat type in the region being high, and/or the total spend atrestaurants of that type in the region being high. The user of themethod may, if desired, tailor the clustering algorithm to seek clustersof one or more of these types, according to the information in whichhe/she is most interested. Note that the diagram is not cluttered withvery thin segments, because such a segment would represent a clusterwith low statistical significance, and such a cluster would not beidentified at all in step 104.

It can be seen that the most statistically significant cluster for thisregion is for exclusive Indian cuisine. This information may be used bysomeone considering opening such a restaurant in the geographicalregion.

Step 104 may be repeated excluding certain types of restaurants, toobtain more detail about the others. For example, typically, the paymenttransactions will include spending both on food and drink at therestaurants. For certain restaurants (e.g. the pubs) the spending ondrink will be a high proportion of spending, so a certain user of themethod may exclude the data in respect of pubs.

Similarly, an operator of restaurants catering to a certain demographic,e.g. single young people, may instruct the server 1 to perform (orrepeat) the method of FIG. 2 using, in step 104, only paymenttransaction data relating to consumers who, according to the database 7,are in that demographic, to obtain data which shows the restaurantspending behaviour of that demographic.

FIG. 4 is a block diagram showing a technical architecture of thepayment network server 1.

The technical architecture includes a processor 222 (which may bereferred to as a central processor unit or CPU) that is in communicationwith memory devices including secondary storage 224 (such as diskdrives), read only memory (ROM) 226, random access memory (RAM) 228. Theprocessor 222 may be implemented as one or more CPU chips. The technicalarchitecture may further comprise input/output (I/O) devices 230, andnetwork connectivity devices 232.

The secondary storage 224 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 228 is not large enough tohold all working data. Secondary storage 224 may be used to storeprograms which are loaded into RAM 228 when such programs are selectedfor execution.

In this embodiment, the secondary storage 224 comprises non-transitoryinstructions operative by the processor 222 to perform variousoperations of the method of the present disclosure. The ROM 226 is usedto store instructions and perhaps data which are read during programexecution. The secondary storage 224, the RAM 228, and/or the ROM 226may be referred to in some contexts as computer readable storage mediaand/or non-transitory computer readable media.

I/O devices 230 may include printers, video monitors, liquid crystaldisplays (LCDs), plasma displays, touch screen displays, keyboards,keypads, switches, dials, mice, track balls, voice recognizers, cardreaders, paper tape readers, or other well-known input devices.

The network connectivity devices 232 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 232 may enable the processor 222 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 222 mightreceive information from the network, or might output information to thenetwork in the course of performing the above-described methodoperations. Such information, which is often represented as a sequenceof instructions to be executed using processor 222, may be received fromand outputted to the network, for example, in the form of a computerdata signal embodied in a carrier wave.

The processor 222 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 224), flash drive, ROM 226, RAM 228, or the network connectivitydevices 232. While only one processor 222 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors.

Although the technical architecture is described with reference to acomputer, it should be appreciated that the technical architecture maybe formed by two or more computers in communication with each other thatcollaborate to perform a task. For example, but not by way oflimitation, an application may be partitioned in such a way as to permitconcurrent and/or parallel processing of the instructions of theapplication. Alternatively, the data processed by the application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of different portions of a data set by the two or morecomputers. In an embodiment, virtualization software may be employed bythe technical architecture 220 to provide the functionality of a numberof servers that is not directly bound to the number of computers in thetechnical architecture 220. In an embodiment, the functionalitydisclosed above may be provided by executing the application and/orapplications in a cloud computing environment. Cloud computing maycomprise providing computing services via a network connection usingdynamically scalable computing resources. A cloud computing environmentmay be established by an enterprise and/or may be hired on an as-neededbasis from a third party provider.

It is understood that by programming and/or loading executableinstructions onto the technical architecture, at least one of the CPU222, the RAM 228, and the ROM 226 are changed, transforming thetechnical architecture in part into a specific purpose machine orapparatus having the novel functionality taught by the presentdisclosure. It is fundamental to the electrical engineering and softwareengineering arts that functionality that can be implemented by loadingexecutable software into a computer can be converted to a hardwareimplementation by well-known design rules.

Whilst the foregoing description has described exemplary embodiments, itwill be understood by those skilled in the art that many variations ofthe embodiment can be made within the scope and spirit of the presentinvention.

1. A computer-implemented method for obtaining geographic regioncharacterization data statistically characterizing the consumptionhabits of consumers in a geographical region, the method including acomputer server: (i) identifying a plurality of consumers associatedwith the region, (ii) obtaining, for each of a plurality of merchantswho perform sales in the region, the merchants being competitors in acommercial sector, corresponding merchant characterization data whichindicates whether the commercial activity of the merchant belongs to oneof more of a set of merchant categories, and (iii) using paymenttransaction data for the identified consumers, and the merchantcharacterization data, to obtain respective geographical regioncharacterization data describing statistically the purchasing behaviorof the identified consumers in relation to the merchant categories.
 2. Amethod according to claim 1 in which the merchants operate restaurantsand the merchant characterization data characterizes the correspondingrestaurants.
 3. A method according to claim 2 in which the merchantcategories include one or more of (a) a plurality of categoriesassociated with respective types of cuisine, (b) a plurality ofcategories indicative of price, and/or (c) a plurality of categoriesassociated with type of restaurant premises.
 4. A method according toclaim 1 which includes a step of ranking the merchants based on thepayment transaction data for the identified consumers.
 5. A methodaccording to claim 4 in which the ranking is based on a popularity indexwhich is a function of any one or more of the total money spent at themerchant, the number of payment transactions to the restaurant, and/orthe average amount spent per transaction.
 6. A method according to claim1 in which the step of obtaining the geographical regioncharacterization data includes a clustering step, to identify one ofmore sets of consumers who meet at least one similarity criterion inrelation to their spending behavior.
 7. A method according to claim 1 inwhich the step of obtaining the geographical region characterizationdata employs demographic information describing the identifiedconsumers, whereby the geographical region characterization informationis indicative the purchasing behavior in relation to the merchantcategories of those of the identified consumers who are in at least onedemographic category.
 8. A method according to claim 1 in which at leastpart of the merchant characterization data used in the step of obtainingthe geographical region characterization data is obtained from aconsumer data website.
 9. A method according to claim 8 in which thestep of obtaining the geographical region characterization data includesidentifying in a database of said merchant characterization data, atleast one of the merchants for whom the merchant characterization datain the database does not meet a completeness criterion, and for theidentified at least one merchant obtaining further merchantcharacterization data from the consumer data website.
 10. A methodaccording to claim 1 in which said step of identifying the plurality ofconsumers associated with the region is performed using both aresidential address for each consumer and payment transaction data forthe consumer.
 11. A method according to claim 1 in which the step ofidentifying the plurality of consumers associated with the regionidentifies consumers who have made purchases in the region and whoseresidence is in the region.
 12. A method according to claim 1 in whichthe step of identifying the plurality of consumers associated with theregion identifies consumers who have made purchases in the region andwhose residence is not in the region.
 13. A computer system forobtaining geographic region characterization data statisticallycharacterizing the consumption habits of consumers in a geographicalregion, the computer system comprising a processor and a data storagedevice storing: (a) program instructions, (b) a payment transactiondatabase comprising payment transaction data describing paymenttransactions made by consumers; (c) a consumer database of datadescribing the consumers; and (d) a merchant database storing merchantcharacterization data in respect of each of plurality of merchants, themerchant characterization data being indicative of whether thecommercial activity of the merchant belongs to one of more of a set ofmerchant categories; the program instructions being operative, uponbeing performed by the processor to cause the processor: (i) using theconsumer database to identify a plurality of consumers associated withthe region, (ii) to extract from the merchant database merchantcharacterization data in respect of a plurality of the merchants whichoperate commercially in the region, and (iii) to use the paymenttransaction data for the identified consumers, and the extractedmerchant characterization data, to obtain respective geographical regioncharacterization data describing statistically the purchasing behaviorof the identified consumers in relation to the merchant categories. 14.A computer system according to claim 13 in which the programinstructions are operative to cause the processor to rank the merchantswhich operate commercially in the region based on payment transactiondata for the identified consumers.
 15. A computer system according toclaim 14 in which the ranking is based on a popularity index which is afunction of any one or more of the total money spent at the merchant,the number of payment transactions to the restaurant, and/or the averageamount spent per transaction.
 16. A computer system according to claim13 in which the program instructions are operative to cause theprocessor perform a clustering step, to identify one of more sets ofconsumers who meet at least one similarity criterion in relation totheir spending behavior.
 17. A computer system according to claim 13 inwhich the consumer database comprises demographic information describingthe consumers, whereby the geographical region characterizationinformation is indicative the purchasing behavior in relation to thecategories of those of the identified consumers who are in at least onedemographic category.
 18. A computer system according to claim 13 inwhich the program instructions are operative to cause the processor toobtain at least part of the merchant characterization data over acommunication network from a consumer data website.
 19. A computersystem according to claim 18 in which the program instructions areoperative to cause the processor to identify in the merchant databasemerchant at least one of the merchants for whom the merchantcharacterization data in the database does not meet a completenesscriterion, and for the identified at least one merchant obtain furthermerchant characterization data from the consumer data website.
 20. Acomputer system according to claim 13 in which the program instructionsare operative to cause the processor identify the plurality of consumersassociated with the region using both a residential address for eachconsumer stored in the consumer database, and payment transaction datastored in the payment transaction database.
 21. A computer systemaccording to claim 13 in which the program instructions are operative tocause the processor to identify the plurality of consumers associatedwith the region as consumers who have made purchases in the region andwho meet a criterion indicative of the consumer residing in the region.22. A computer system according to claim 13 in which the programinstructions are operative to cause the processor to identify theplurality of consumers associated with the region as consumers who havemade purchases in the region and who meet a criterion indicative of theconsumer not residing in the region.